SCIS & ISIS
SCIS & ISIS 2010
セッションID: SA-B4-4
会議情報
Seeding Method based on Independent Component Analysis for k-Means Clustering
Takashi Onoda*Miho SakaiSeiji Yamada
著者情報
キーワード: k-Means, k-Means++, ICA
会議録・要旨集 フリー

詳細
抄録
The k-means method is a widely used clustering technique because of its simplicity and speed. However, the clustering result depends heavily on the chosen initial value. In this report, we propose a seeding method with independent component analysis for the k-means method. Using a benchmark dataset, we evaluate the performance of our proposed method and compare it with other seeding methods.
著者関連情報
© 2010 Japan Society for Fuzzy Theory and Intelligent Informatics
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